Malware, or malicious code that may be utilized to infect computing systems and/or compromise the way that they work, is evolving as a class of software. In response to the rapid growth of this type of software, anti-malware solutions have been developed that are designed to mitigate the damaging effects to a computing system exposed to malware and/or other harmful or unwanted content. However, current anti-malware solutions are primarily reactive. That is, no matter how proficient a solution is at mitigating harmful effects, current solutions are unable to adequately detect malware before it causes actual harm. Further, most anti-malware solutions are rather time consuming processes as a significant amount of manual research is involved. Accordingly, once content is identified as harmful, malicious, or unwanted, and/or a malevolent web site or entity is identified, computing system users may be exposed to additional hours or days of risk before an adequate blocking of the harmful content is effected. Additionally, identification of harmful, malicious, or unwanted content in association with a particular web site does not in and of itself solve the problem as the offending entity may simply associate a different harmful, malicious, or unwanted content item that is not yet known to be harmful with the web site.
For an anti-malware solution to be optimized, new malware and new web sites distributing malware (and other harmful or unwanted content) must be identified in as close to real-time as possible, ideally before a large number of computing devices become infected. Current anti-malware solutions are simply unable to identify harmful, malicious, or unwanted content in a timely-enough fashion.